Dense correspondence matching.  Many vision tasks can be considered as a correspondence matching problem,  stereo matching [1]}, [2]} and flow estimation [3]}, [4]},
where the essence is the correlation measures for paired images.
In particular, Nikolaus  [5]} directly regress disparity maps from an 1D correlation volume.
Alex  [6]} concatenate features of image frames to form the correlation volume.
The work [7]} learns to fuse multiple disparity maps to provide a more accurate correlation volume.
The recent work on optical flow estimation [8]} adopts the similar idea but constructs a multi-scale correlation volume.
In their model, coarser correlation volumes are constructed by pooling operations over finer correlation volumes.
In this way, their models capture distant pixel displacements while preserving fine-grained image details.
